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AI’s Impending Data Crisis

AI may soon be a trillion-dollar industry, but it faces a significant bottleneck as available training data approaches depletion. While there’s a race to create larger models, a critical issue remains largely unaddressed: the potential exhaustion of usable training data in a few years.

Key Insights

• Data Scarcity: Training datasets have grown at an annual rate of 3.7x, with projections indicating that the quality public data supply could deplete between 2026 and 2032.
• Labeling Market Growth: The data labeling market is expected to expand from $3.7 billion in 2024 to $17.1 billion by 2030, amid tightening access to real human data.
• Synthetic Data Limitations: Synthetic data cannot adequately replace authentic human data due to feedback loops and a lack of real-world nuance, posing risks to AI model performance.
• Shift in Power: As models become commoditized, the ownership and control over unique, high-quality datasets will distinguish AI firms in the competitive landscape.

The Bottleneck of Training Data

Since 2010, training datasets for large language models have expanded at about 3.7 times annually. With the depletion of high-quality public training data projected within the next decade, the urgency for fresh, diverse, and unbiased datasets is critical.

As companies clamp down on data access and governments impose regulations on data scraping, the landscape for AI development is changing. Public sentiment is shifting against utilizing user-generated content without compensation, making it essential to rethink data sourcing strategies.

While synthetic data is a suggested alternative, it has associated risks, including reduced performance stability. As a result, genuine human-generated data becomes increasingly valuable, yet access is heavily restricted among major platforms like Meta and Google.

Why This Matters for AI Development

The AI value chain encompasses both model creation and data acquisition. Recently, the focus has predominantly been on model development. However, as model sizes approach their limits and alternatives become available, differentiating factors lie in unique datasets. Owners of high-quality datasets can create innovative value and drive more effective model training aligned with audience needs.

Control Will Dictate AI Advancement

We are entering a phase where control over data will define power in the AI realm. As the quest to improve AI models intensifies, the critical challenge will not be computational power but the sourcing of genuine, useful, and legally allowable data.

Thus, moving forward, it’s essential to shift the focus from who builds the models to who provides the data, as the future of AI will depend on its input.

About the Author

Max Li is the founder and CEO at OORT, a decentralized AI data cloud. Li holds extensive expertise in engineering and innovation, with over 200 patents and a background that includes work on 4G LTE and 5G systems with Qualcomm Research. He is also a professor and author of “Reinforcement Learning for Cyber-physical Systems.”